Despite its high significance, the clinical utilization of image registration remains limited because of its lengthy
execution time and a lack of easy access. The focus of this work was twofold. First, we accelerated our course-to-fine,
volume subdivision-based image registration algorithm by a novel parallel implementation that maintains the accuracy of
our uniprocessor implementation. Second, we developed a thin-client computing model with a user-friendly interface to
perform rigid and nonrigid image registration. Our novel parallel computing model uses the message passing interface
model on a 32-core cluster. The results show that, compared with the uniprocessor implementation, the parallel
implementation of our image registration algorithm is approximately 5 times faster for rigid image registration and
approximately 9 times faster for nonrigid registration for the images used. To test the viability of such systems for
clinical use, we developed a thin client in the form of a plug-in in OsiriX, a well-known open source PACS workstation
and DICOM viewer, and used it for two applications. The first application registered the baseline and follow-up MR brain images, whose subtraction was used to track progression of multiple sclerosis. The second application registered pretreatment PET and intratreatment CT of radiofrequency ablation patients to demonstrate a new capability of multimodality imaging guidance. The registration acceleration coupled with the remote implementation using a thin client should ultimately increase accuracy, speed, and access of image registration-based interpretations in a number of diagnostic and interventional applications.
Knee-related injuries including meniscal tears are common in both young athletes and the aging population and require
accurate diagnosis and surgical intervention when appropriate. With proper techniques and radiologists' experienced
skills, confidence in detection of meniscal tears can be quite high. However, for radiologists without musculoskeletal
training, diagnosis of meniscal tears can be challenging. This paper develops a novel computer-aided detection (CAD)
diagnostic system for automatic detection of meniscal tears in the knee. Evaluation of this CAD system using an
archived database of images from 40 individuals with suspected knee injuries indicates that the sensitivity and
specificity of the proposed CAD system are 83.87% and 75.19%, respectively, compared to the mean sensitivity and
specificity of 77.41% and 81.39%, respectively obtained by experienced radiologists in routine diagnosis without using
the CAD. The experimental results suggest that the developed CAD system has great potential and promise in automatic
detection of both simple and complex meniscal tears of knees.
Osteoarthritis (OA) is the most common form of arthritis and a major cause of morbidity affecting millions of adults in
the US and world wide. In the knee, OA begins with the degeneration of joint articular cartilage, eventually resulting in
the femur and tibia coming in contact, and leading to severe pain and stiffness. There has been extensive research
examining 3D MR imaging sequences and automatic/semi-automatic techniques for 2D/3D articular cartilage
extraction. However, in routine clinical practice the most popular technique still remain radiographic examination and
qualitative assessment of the joint space. This may be in large part because of a lack of tools that can provide clinically
relevant diagnosis in adjunct (in near real time fashion) with the radiologist and which can serve the needs of the
radiologists and reduce inter-observer variation. Our work aims to fill this void by developing a CAD application that
can generate clinically relevant diagnosis of the articular cartilage damage in near real time fashion. The algorithm
features a 2D Active Shape Model (ASM) for modeling the bone-cartilage interface on all the slices of a Double Echo
Steady State (DESS) MR sequence, followed by measurement of the cartilage thickness from the surface of the bone,
and finally by the identification of regions of abnormal thinness and focal/degenerative lesions. A preliminary
evaluation of CAD tool was carried out on 10 cases taken from the Osteoarthritis Initiative (OAI) database. When
compared with 2 board-certified musculoskeletal radiologists, the automatic CAD application was able to get
segmentation/thickness maps in little over 60 seconds for all of the cases. This observation poses interesting
possibilities for increasing radiologist productivity and confidence, improving patient outcomes, and applying more
sophisticated CAD algorithms to routine orthopedic imaging tasks.
Knee-related injuries involving the meniscal or articular cartilage are common and require accurate diagnosis and
surgical intervention when appropriate. With proper techniques and experience, confidence in detection of meniscal
tears and articular cartilage abnormalities can be quite high. However, for radiologists without musculoskeletal training,
diagnosis of such abnormalities can be challenging. In this paper, the potential of improving diagnosis through
integration of computer-aided detection (CAD) algorithms for automatic detection of meniscal tears and articular
cartilage injuries of the knees is studied. An integrated approach in which the results of algorithms evaluating either
meniscal tears or articular cartilage injuries provide feedback to each other is believed to improve the diagnostic
accuracy of the individual CAD algorithms due to the known association between abnormalities in these distinct
anatomic structures. The correlation between meniscal tears and articular cartilage injuries is exploited to improve the
final diagnostic results of the individual algorithms. Preliminary results from the integrated application are encouraging
and more comprehensive tests are being planned.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.